Case Studies in Environmental Statistics: (132 Lecture Notes in Statistics)

This book offers a set of case studies exemplifying the broad range of statistical science used in evnironmental studies and application. The case studies can be used for graduate courses in environmental statistics, as a resource for courses in statistics using genuine examples to illustrate statistical methodology and theory, and for courses in environmental science. Not only are these studies valuable for teaching about an essential cross-disciplinary activity, but also they can be used to spur new research along directions exposed in these examples. The studies reported here resulted from a program of research carried on by the National Institute of Statistical Sciences (NISS) during the years 1992-1996. NISS was created in 1991 as an initiative of the national statistics organizations, with the mission to renew and focus efforts of statistical science on important cross-disciplinary problems. One of NISS' first projects was a cooperative research effort with the U.S. EPA on problems of great interest to environmental science and regulation, surely one of today's most important cross-disciplinary activities. Douglas Nychka is Professor of Statistics at North Carolina State University and currently the Directory of the Geophysical Statistics Project at the National Center for Atmospheric Research. He has made contributions to the modeling of ozone pollution and the design of monitoring networks and has been author on more than 40 scientific papers. Walter W. Piegorsch is Professor of Statistics at the University of South Carolina and studies problems in design and analysis of environmental toxicity studies, low-dose risk extrapolation, and combining environmental information.

Table of Contents:
1 Introduction: Problems in Environmental Monitoring and Assessment.- 1 Statistical Methods for Environmental Monitoring and Assessment.- 2 Outline of Case Studies.- 3 Sources of Data and Software.- Acknowledgments.- 2 Modeling Ozone in the Chicago Urban Area.- 1 Introduction.- 2 Data Sources.- 3 Trend Analysis and Adjustment.- 4 Trends from Semiparametric Models.- 5 Trends in Exceedances.- 6 Summary.- Acknowledgments.- References.- 3 Regional and Temporal Models for Ozone Along the Gulf Coast.- 1 Introduction.- 2 Diurnal Variation in Ozone.- 3 Meteorological Clusters and Ozone.- 4 Regional Variation in Ozone.- 5 Summary.- 6 Future Directions.- References.- 4 Design of Air-Quality Monitoring Networks.- 1 Introduction.- 2 Data.- 3 Spatial Models.- 4 Thinning a Small Urban Network.- 5 Adding Rural Stations to Northern Illinois.- 6 Modifying Regional Networks.- 7 Scientific Contributions and Discussion.- References.- 5 Estimating Trends in the Atmospheric Deposition of Pollutants.- 1 Introduction.- 2 Monitoring Data.- 3 Case Studies.- 4 Future Research.- Acknowledgment.- References.- 6 Airborne Particles and Mortality.- 1 Introduction.- 2 Statistical Studies of Particles and Mortality.- 3 An Example: Data from Birmingham, Alabama.- 4 Results for Birmingham.- 5 Comparisons with Other Cities.- 6 Conclusions: Accidental Association or Causal Connection.- References.- 7 Categorical Exposure-Response Regression Analysis of Toxicology Experiments.- 1 Introduction.- 2 The Tetrachloroethylene Database.- 3 Statistical Models for Exposure-Response Relationships.- 4 Computing Software: CatReg.- 5 Application to Tetrachloro ethylene Data.- 6 Conclusions.- 7 Future Directions.- Acknowledgments.- References.- 8 Workshop: Statistical Methods for Combining Environmental Information.- 1 The NISS-USEPA Workshop Series.- 2 Combining Environmental Information.- 3 Combining Environmental Epidemiology Information.- 4 Combining Environmental Assessment Information.- 5 Combining Environmental Monitoring Data.- 6 Future Directions.- References.- A Appendix A: FUNFITS, Data Analysis and Statistical Tools for Estimating Functions Douglas Nychka, Perry D. Haaland, Michael A. O’Connell, Stephen Ellner.- 1 Introduction.- 2 What’s So Special About FUNFITS?.- 2.1 An Example.- 3 A Basic Model for Regression.- 4 Thin-Plate Splines: tps.- 4.1 Determining the Smoothing Parameter.- 4.2 Approximate Splines for Large Data Sets.- 4.3 Standard Errors.- 5 Spatial Process Models: krig.- 5.1 Specifying the Covariance Function.- 5.2 Some Examples of Spatial Process Estimates.- Acknowledgments.- References.- B Appendix B: DI, A Design Interface for Constructing and Analyzing Spatial Designs Nancy Saltzman, Douglas Nychka.- 1 Introduction.- 2 An Example.- 3 How DI Works.- 3.1 Network Objects.- 3.2 The Design Editor.- 3.3 User Modifications.- C Appendix C: Workshops Sponsored Through the EPA/NISS Cooperative Agreement.- D Appendix D: Participating Scientists in the Cooperative Agreement.